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About

Srikumar Ramalingam is a computer vision and robotics researcher whose work spans 3D scene understanding, pose estimation, camera tracking, and sensor-based registration — areas that sit at the intersection of geometric computing and practical robotic applications. He is perhaps best known for his voting-based pose estimation algorithm (2012), which leverages oriented 3D point pairs to enable robust object recognition and localization using depth sensors, garnering nearly 200 citations and establishing itself as a foundational contribution to the robotics and vision communities during the rapid rise of 3D sensing technologies. Ramalingam has made significant theoretical contributions to point-to-plane registration, developing minimal solution frameworks that underpin efficient and accurate 3D alignment — work that spans from early formulations in 2010 through continued refinement. His research on RGB-D camera tracking using hybrid point-and-plane representations demonstrated practical advantages in both indoor and outdoor environments, reflecting a consistent commitment to bridging mathematical rigor with real-world deployability. More recently, he has pushed into probabilistic deep learning, exploring non-parametric representations of pose uncertainty on rotation manifolds for single-image estimation. Collectively, his body of work reflects a researcher deeply invested in making 3D perception more robust, efficient, and theoretically grounded for the next generation of autonomous systems.